Instructions to use Eli-Rhm/Reward with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Eli-Rhm/Reward with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Eli-Rhm/Reward")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Eli-Rhm/Reward") model = AutoModelForSequenceClassification.from_pretrained("Eli-Rhm/Reward", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download notes.txt from Eli-Rhm/Reward: direct link, hf CLI and curl.
- Browser
- Download file 86 Bytes
-
https://huggingface.co/Eli-Rhm/Reward/resolve/main/notes.txt
- Command line
-
hf download hf://Eli-Rhm/Reward/notes.txt
-
curl -L -o notes.txt https://huggingface.co/Eli-Rhm/Reward/resolve/main/notes.txt
86 Bytes
| Dataset size from 10K to 40K samples | |
| from 5 to 8 epochs | |
| Lora.Dropout from 0.1 to 0.2 |